Computerised Decision-Making Support System Based on Data Fusion for Machinery System’s Management and Maintenance

Abstract:

In the present fast-paced world of business in a highly competitive marketplace, the focus on increasing the efficiency of business processes seems to be a reasonable challenge. For this reason in order to optimize production costs, there is an increasing trend toward maximising the use of the operational capabilities of the equipment – technological line components – with the prevention of failure events and their consequences in the form of costly repairs or replacements. A condition-based maintenance (CBM) approach allows to globally monitor, maintain and control operation of the whole complex of equipment and/or processes. The CBM effectively supports decision-making process concerning their further use or determination of the optimum repair/replacement schedule. The presented decision support system is dedicated for an underground copper mine, where the network of belt conveyors is a critical part of the production process. Due to complexity of mechanical system, harsh mining environment and presence of many degradation factors, development of the effective CBM system seems to be justified. It requires the integration of data from different sources, adaptation of advanced data mining techniques, procedures or various diagnostic methods. Because of the multidimensional nature of diagnostic data and diversified technical configurations of the facilities, it was necessary to develop and implement multivariate analytical models based on artificial intelligence techniques. Consequently, it allows to achieve improvement of efficiency of transportation network and reduction of repairs costs and unplanned breakdowns. In this paper we will briefly refer to all these issues.

• Periodical: Applied Mechanics and Materials (Volume 683)
• Pages: 108-113
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.683.108
• Online since: October 2014
• Authors: Pawel Stefaniak*, Radosław Zimroz, Walter Bartelmus, Monika Hardygóra
• Keywords: Belt Conveyor, Data Mining (DM), Diagnostics, Management Support System, Temporal GIS

References

[1] G. Lodewijks, Strategies for Automated Maintenance of Belt Conveyor Systems, Bulk Solids Handling 24(1) (2004) 16-22.

[2] M. Kacprzak, P. Kulinowski, D. Wędrychowicz, Computerized information system used for management of mining belt conveyors operation, Eksploatacja i Niezawodnosc – Maintenance and Reliability 50(2) (2011) 81-93.

[3] P.K. Stefaniak, R. Zimroz, R. Król, J. Górniak- Zimroz, W. Bartelmus, M. Hardygóra, Some Remarks on Using Condition Monitoring for Spatially Distributed Mechanical System Belt Conveyor Network in Underground Mine – A Case Study, Condition Monitoring of Machinery in Non-Stationary Operations 4 (2012).

DOI: 10.1007/978-3-642-28768-8_51

[4] R. Blazej, L. Jurdziak , R. Zimroz, Novel approaches for processing of multi-channels NDT signals for damage detection in conveyor belts with steel cords, Key Engineering Materials 569-570 (2013) 978 – 985.

DOI: 10.4028/www.scientific.net/kem.569-570.978

[5] P.K. Stefaniak, A. Wylomanska, J. Obuchowski, R. Zimroz, Procedures for decision thresholds finding in maintenance management of belt conveyor system – statistical modeling of diagnostic data, in: 12th International Symposium Continuous Surface Mining, Aachen, (2014).

DOI: 10.1007/978-3-319-12301-1_34

[6] D. Galar, A. Gustafson, B. Tormos, L. Berges, Maintenance Decision Making based on Different types of Data fusion, Eksploatacja i Niezawodnosc – Maintenance and Reliability 14(2) (2012; ) 135-144.

[7] A.W. Labib, A decision analysis model for maintenance policy selection using a CMMS, Journal of Quality in Maintenance Engineering 10(3) (2004) 191-202.

DOI: 10.1108/13552510410553244

[8] D. Mather, CMMS: a time saving implementation process, CRC Press, New York, (2002).

[9] C.D. O'Donoghue, J.G. Prendergast, Implementation and benefits of introducing a computerised maintenance management system into a textile manufacturing company, Journal of Materials Processing Technology 153-154 (2004) 226-232.

DOI: 10.1016/j.jmatprotec.2004.04.022

[10] W. Frawley, G. Piatetsky-Shapiro, C. Matheus, Knowledge discovery in databases: An overview, AI Magazine 13 (1992) 57–70.

[11] A. Jablonski, T. Barszcz, Validation of vibration measurements for heavy duty machinery diagnostics, Mechanical Systems and Signal Processing 38(1) (2013) 248-263.

DOI: 10.1016/j.ymssp.2013.01.015

[12] A. Bartkowiak, R. Zimroz, Dimensionality reduction via variables selection - Linear and nonlinear approaches with application to vibration-based condition monitoring of planetary gearbox Applied Acoustics 77 (2014) 169 – 177.

DOI: 10.1016/j.apacoust.2013.06.017

[13] J. Obuchowski, A. Wyłomańska, R. Zimroz, Stochastic modeling of time series with application to local damage detection in rotating machinery, Key Eng. Mat. 569/570 (2013) 441-448.

DOI: 10.4028/www.scientific.net/kem.569-570.441

[14] R. A. Makowski, R. Zimroz, Adaptive bearings vibration modelling for diagnosis Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6943 LNAI, pp.248-259.

DOI: 10.1007/978-3-642-23857-4_26